diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..3edb0b5
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,34 @@
+# Include any files or directories that you don't want to be copied to your
+# container here (e.g., local build artifacts, temporary files, etc.).
+#
+# For more help, visit the .dockerignore file reference guide at
+# https://docs.docker.com/engine/reference/builder/#dockerignore-file
+
+**/.DS_Store
+**/__pycache__
+**/.venv
+**/.classpath
+**/.dockerignore
+**/.env
+**/.git
+**/.gitignore
+**/.project
+**/.settings
+**/.toolstarget
+**/.vs
+**/.vscode
+**/*.*proj.user
+**/*.dbmdl
+**/*.jfm
+**/bin
+**/charts
+**/docker-compose*
+**/compose*
+**/Dockerfile*
+**/node_modules
+**/npm-debug.log
+**/obj
+**/secrets.dev.yaml
+**/values.dev.yaml
+LICENSE
+README.md
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..7b53c99
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,56 @@
+# syntax=docker/dockerfile:1
+
+# Comments are provided throughout this file to help you get started.
+# If you need more help, visit the Dockerfile reference guide at
+# https://docs.docker.com/engine/reference/builder/
+
+ARG PYTHON_VERSION=3.10.14
+FROM python:${PYTHON_VERSION}-bullseye as base
+
+# Prevents Python from writing pyc files.
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Keeps Python from buffering stdout and stderr to avoid situations where
+# the application crashes without emitting any logs due to buffering.
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# Create a non-privileged user that the app will run under.
+# See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#user
+ARG UID=10001
+RUN adduser \
+    --disabled-password \
+    --gecos "" \
+    --home "/nonexistent" \
+    --shell "/sbin/nologin" \
+    --no-create-home \
+    --uid "${UID}" \
+    appuser
+
+RUN apt-get -y update
+RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC \
+    apt-get -y dist-upgrade
+
+RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC \
+    apt-get -y install libeccodes-dev libhdf5-serial-dev pkg-config cmake g++-10 gcc-10
+
+# Download dependencies as a separate step to take advantage of Docker's caching.
+# Leverage a cache mount to /root/.cache/pip to speed up subsequent builds.
+# Leverage a bind mount to requirements.txt to avoid having to copy them into
+# into this layer.
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,source=requirements.txt,target=requirements.txt \
+    python -m pip install -r requirements.txt
+
+# Switch to the non-privileged user to run the application.
+USER appuser
+
+# Copy the source code into the container.
+COPY . .
+
+# Expose the port that the application listens on.
+EXPOSE 8890
+
+# Run the application.
+CMD python -m example_forecast
diff --git a/README.md b/README.md
index cbe5d3b..21aedb5 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,17 @@ the composable model framework. Example PV model construction is shown below.
 The API for fit, predict, and metrics is reduced to specifying a start and end times for a given location.
 The model must construct feature data using column transforms. Having done so, forecasting as a service become trivial.
 
+## Getting started
+
+Users can verify the code works with the example_forecast.py script by running `docker compose up --build`
+The log output from the container will include feature and weather data as well as predicted values. The script
+takes several minutes to run because the weather data is large.
+
+The dockerfile included in the project will run the example_forecast.py which demonstrates both the machine
+learning model for AMI meter forecasting and the physics based PV model using PySam. Users can then choose between
+their local working environment and containerized environment to extend and experiment with the time series models
+library.
+
 ## Installation
 
 This library is designed for use by technical engineers and data scientists. It takes advantage of the Python
@@ -70,8 +81,6 @@ jupyter notebook --NotebookApp.ip=0.0.0.0
 
 This will print a URL, which you can open in your browser. Then open the example notebook and execute the cells in the demonstration to get acquainted with the functionality.
 
-<!-- TODO: add docker executable image in July 2024 -->
-
 ## Usage
 Models can be composed of mixins for various estimators and forecast processes. These composable
 pieces can be put together in different ways to solve many problems. The RegularTimeSeriesModel is the
@@ -159,11 +168,18 @@ using machine learning models like xgboost too.
 
 ```python
 pv_config = dict(
-    site_config_mapping="RESOURCE_SELF",
-    site_meter_mapping=None,
-    site_latlong_mapping="RESOURCE_SELF",
-    source_mode="12_hour_horizon",
-    lags=None,
+  lags=None,
+  site_config_mapping={
+    "capybara": ["/app/pv_site.json"],
+  },
+  site_latlong_mapping={
+    "capybara": dict(
+      latitude=40.0,
+      longitude=-100.0,
+    ),
+  },
+  site_meter_mapping=None,
+  source_mode="12_hour_horizon",
 )
 
 class PVForecastModel(
@@ -186,7 +202,6 @@ Engineers and data scientists commonly use an interactive web-based development
 An [example notebook](https://github.com/SETO2243/forecasting/blob/main/example.ipynb) is provided in this GitHub
 repository which demonstrates the core capabilities of the time series models library developed for the SETO project. 
 
-<!-- TODO: Add screen shot of dockerized output, July 2024-->
 
 ## Input Data
 
diff --git a/compose.yaml b/compose.yaml
new file mode 100644
index 0000000..3cd3898
--- /dev/null
+++ b/compose.yaml
@@ -0,0 +1,16 @@
+# Comments are provided throughout this file to help you get started.
+# If you need more help, visit the Docker compose reference guide at
+# https://docs.docker.com/compose/compose-file/
+
+# Here the instructions define your application as a service called "server".
+# This service is built from the Dockerfile in the current directory.
+# You can add other services your application may depend on here, such as a
+# database or a cache. For examples, see the Awesome Compose repository:
+# https://github.com/docker/awesome-compose
+services:
+  server:
+    build:
+      context: .
+    ports:
+      - 8890:8890
+
diff --git a/example_forecast.py b/example_forecast.py
new file mode 100644
index 0000000..ea55b75
--- /dev/null
+++ b/example_forecast.py
@@ -0,0 +1,90 @@
+import logging
+import numpy as np
+
+from time_series_models.time_series_models import RegularTimeSeriesModel
+from time_series_models.processes import AmiHourlyForecast, PVForecast
+from time_series_models.estimators import (
+    XgbRegressor,
+    IdentityRegressor,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def run_forecast_example():
+
+    logger.info(
+        "Starting forecast example for AMI meter forecast with XgBoost estimator!",
+    )
+
+    class XgbModel(AmiHourlyForecast, XgbRegressor, RegularTimeSeriesModel):
+        pass
+
+    config = dict(
+        lags=np.array([24, 48, 168], dtype="timedelta64[h]"),
+        day_of_week=True,
+        harmonics=np.array([24, 168, 365 * 24], dtype="timedelta64[h]"),
+        met_vars=["t", "r2"],
+        met_horizon=12,
+        mapping=dict(p2ulv18716=dict(latitude=35.0, longitude=-75.0)),
+    )
+    instance = XgbModel(**config)
+
+    instance.fit("2021-01-15", "2021-01-31", "p2ulv18716")
+
+    logger.info("Trained instance: %s", instance.model)
+
+    features_df = instance.features_dataframe("2021-02-01", "2021-02-05", "p2ulv18716")
+    logger.info("Features data: %s", features_df)
+
+    predicted_df = instance.predict_dataframe(
+        "2021-01-01", "2021-02-05", "p2ulv18716", range=True
+    )
+
+    logger.info("Predicted: %s", predicted_df)
+
+    logger.info(
+        "Starting forecast example for PV physical forecast!",
+    )
+
+    pv_config = dict(
+        lags=None,
+        site_config_mapping={
+            "capybara": ["/app/pv_site.json"],
+        },
+        site_latlong_mapping={
+            "capybara": dict(
+                latitude=40.0,
+                longitude=-100.0,
+            ),
+        },
+        site_meter_mapping=None,
+        source_mode="12_hour_horizon",
+    )
+
+    class PVForecastModel(
+        PVForecast,
+        IdentityRegressor,
+        RegularTimeSeriesModel,
+    ):
+        pass
+
+    pv_instance = PVForecastModel(**pv_config)
+    pv_instance.model
+
+    pv_instance.fit("2021-01-15", "2021-01-16", "capybara")
+
+    pv_hrrr_df = pv_instance.hrrr_fetcher.source_loader(
+        np.datetime64("2021-02-01"), np.datetime64("2021-02-05"), "capybara"
+    )
+    logger.info("PV HRRR Data: %s", pv_hrrr_df)
+
+    pv_df = pv_instance.predict_dataframe("2021-02-01", "2021-02-05", "capybara")
+    logger.info("pv predictions: %s", pv_df)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    run_forecast_example()
+    logging.info("All done!")
+    exit(0)
diff --git a/pv_site.json b/pv_site.json
new file mode 100644
index 0000000..5ff7e14
--- /dev/null
+++ b/pv_site.json
@@ -0,0 +1,9816 @@
+{
+  "__camus_json_type__": "pv_physical_model.PySamPhysicalModel",
+  "__camus_json_data__": {
+    "class": "PvSamV1PhysicalModel",
+    "name": "camus",
+    "exported": {
+      "SolarResource": {
+        "albedo": [
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2,
+          0.2
+        ],
+        "albedo_spatial": [
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+          [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]
+        ],
+        "irrad_mode": 0,
+        "sky_model": 2,
+        "use_spatial_albedos": 0,
+        "use_wf_albedo": 1.0
+      },
+      "Losses": {
+        "acwiring_loss": 1,
+        "calculate_bifacial_electrical_mismatch": 1,
+        "calculate_rack_shading": 0,
+        "dcoptimizer_loss": 0,
+        "en_snow_model": 0,
+        "subarray1_dcwiring_loss": 2,
+        "subarray1_diodeconn_loss": 0.5,
+        "subarray1_electrical_mismatch": 0,
+        "subarray1_mismatch_loss": 2,
+        "subarray1_nameplate_loss": 0,
+        "subarray1_rack_shading": 0,
+        "subarray1_rear_soiling_loss": 0,
+        "subarray1_soiling": [
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5
+        ],
+        "subarray1_tracking_loss": 0,
+        "subarray2_dcwiring_loss": 2,
+        "subarray2_diodeconn_loss": 0.5,
+        "subarray2_electrical_mismatch": 0,
+        "subarray2_mismatch_loss": 2,
+        "subarray2_nameplate_loss": 0,
+        "subarray2_rack_shading": 0,
+        "subarray2_rear_soiling_loss": 0,
+        "subarray2_soiling": [
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5
+        ],
+        "subarray2_tracking_loss": 0,
+        "subarray3_dcwiring_loss": 2,
+        "subarray3_diodeconn_loss": 0.5,
+        "subarray3_electrical_mismatch": 0,
+        "subarray3_mismatch_loss": 2,
+        "subarray3_nameplate_loss": 0,
+        "subarray3_rack_shading": 0,
+        "subarray3_rear_soiling_loss": 0,
+        "subarray3_soiling": [
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5
+        ],
+        "subarray3_tracking_loss": 0,
+        "subarray4_dcwiring_loss": 2,
+        "subarray4_diodeconn_loss": 0.5,
+        "subarray4_electrical_mismatch": 0,
+        "subarray4_mismatch_loss": 2,
+        "subarray4_nameplate_loss": 0,
+        "subarray4_rack_shading": 0,
+        "subarray4_rear_soiling_loss": 0,
+        "subarray4_soiling": [
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5,
+          5
+        ],
+        "subarray4_tracking_loss": 0,
+        "transformer_load_loss": 0,
+        "transformer_no_load_loss": 0,
+        "transmission_loss": 0
+      },
+      "Lifetime": {},
+      "SystemDesign": {
+        "enable_mismatch_vmax_calc": 0,
+        "inverter_count": 3,
+        "subarray1_azimuth": 0,
+        "subarray1_backtrack": 0,
+        "subarray1_gcr": 0.3,
+        "subarray1_modules_per_string": 18,
+        "subarray1_monthly_tilt": [
+          40,
+          40,
+          40,
+          20,
+          20,
+          20,
+          20,
+          20,
+          20,
+          40,
+          40,
+          40
+        ],
+        "subarray1_mppt_input": 1,
+        "subarray1_nstrings": 320,
+        "subarray1_rotlim": 45,
+        "subarray1_slope_azm": 0,
+        "subarray1_slope_tilt": 0,
+        "subarray1_tilt": 0,
+        "subarray1_tilt_eq_lat": 0,
+        "subarray1_track_mode": 1,
+        "subarray2_azimuth": 180,
+        "subarray2_backtrack": 0,
+        "subarray2_enable": 0,
+        "subarray2_gcr": 0.3,
+        "subarray2_modules_per_string": 0,
+        "subarray2_monthly_tilt": [
+          40,
+          40,
+          40,
+          20,
+          20,
+          20,
+          20,
+          20,
+          20,
+          40,
+          40,
+          40
+        ],
+        "subarray2_mppt_input": 1,
+        "subarray2_nstrings": 0,
+        "subarray2_rotlim": 45,
+        "subarray2_slope_azm": 0,
+        "subarray2_slope_tilt": 0,
+        "subarray2_tilt": 20,
+        "subarray2_tilt_eq_lat": 0,
+        "subarray2_track_mode": 0,
+        "subarray3_azimuth": 180,
+        "subarray3_backtrack": 0,
+        "subarray3_enable": 0,
+        "subarray3_gcr": 0.3,
+        "subarray3_modules_per_string": 0,
+        "subarray3_monthly_tilt": [
+          40,
+          40,
+          40,
+          20,
+          20,
+          20,
+          20,
+          20,
+          20,
+          40,
+          40,
+          40
+        ],
+        "subarray3_mppt_input": 1,
+        "subarray3_nstrings": 0,
+        "subarray3_rotlim": 45,
+        "subarray3_slope_azm": 0,
+        "subarray3_slope_tilt": 0,
+        "subarray3_tilt": 20,
+        "subarray3_tilt_eq_lat": 0,
+        "subarray3_track_mode": 0,
+        "subarray4_azimuth": 180,
+        "subarray4_backtrack": 0,
+        "subarray4_enable": 0,
+        "subarray4_gcr": 0.3,
+        "subarray4_modules_per_string": 0,
+        "subarray4_monthly_tilt": [
+          40,
+          40,
+          40,
+          20,
+          20,
+          20,
+          20,
+          20,
+          20,
+          40,
+          40,
+          40
+        ],
+        "subarray4_mppt_input": 1,
+        "subarray4_nstrings": 0,
+        "subarray4_rotlim": 45,
+        "subarray4_slope_azm": 0,
+        "subarray4_slope_tilt": 0,
+        "subarray4_tilt": 20,
+        "subarray4_tilt_eq_lat": 0,
+        "subarray4_track_mode": 0,
+        "system_capacity": 1600.0
+      },
+      "Shading": {
+        "subarray1_shade_mode": 0,
+        "subarray2_shade_mode": 0,
+        "subarray3_shade_mode": 0,
+        "subarray4_shade_mode": 0
+      },
+      "Layout": {
+        "module_aspect_ratio": 1.7,
+        "subarray1_mod_orient": 0,
+        "subarray1_nmodx": 7,
+        "subarray1_nmody": 2,
+        "subarray2_mod_orient": 0,
+        "subarray2_nmodx": 9,
+        "subarray2_nmody": 2,
+        "subarray3_mod_orient": 0,
+        "subarray3_nmodx": 9,
+        "subarray3_nmody": 2,
+        "subarray4_mod_orient": 0,
+        "subarray4_nmodx": 9,
+        "subarray4_nmody": 2
+      },
+      "Module": {
+        "module_model": 0
+      },
+      "SimpleEfficiencyModuleModel": {
+        "spe_a": -3.56,
+        "spe_area": 1.95622,
+        "spe_b": -0.075,
+        "spe_bifacial_ground_clearance_height": 1,
+        "spe_bifacial_transmission_factor": 0.013,
+        "spe_bifaciality": 0.65,
+        "spe_dT": 3,
+        "spe_eff0": 15.6,
+        "spe_eff1": 15.6,
+        "spe_eff2": 15.6,
+        "spe_eff3": 15.6,
+        "spe_eff4": 15.6,
+        "spe_fd": 1,
+        "spe_is_bifacial": 0,
+        "spe_module_structure": 0,
+        "spe_rad0": 200,
+        "spe_rad1": 400,
+        "spe_rad2": 600,
+        "spe_rad3": 800,
+        "spe_rad4": 1000,
+        "spe_reference": 4,
+        "spe_temp_coeff": -0.41,
+        "spe_vmp": 36.8,
+        "spe_voc": 45
+      },
+      "CECPerformanceModelWithModuleDatabase": {
+        "cec_a_ref": 2.42033,
+        "cec_adjust": 4.86979,
+        "cec_alpha_sc": 0.002492,
+        "cec_area": 1.631,
+        "cec_array_cols": 10,
+        "cec_array_rows": 1,
+        "cec_backside_temp": 20,
+        "cec_beta_oc": -0.16975,
+        "cec_bifacial_ground_clearance_height": 1,
+        "cec_bifacial_transmission_factor": 0.013,
+        "cec_bifaciality": 0.65,
+        "cec_gamma_r": -0.31,
+        "cec_gap_spacing": 0.05,
+        "cec_heat_transfer": 0,
+        "cec_height": 0,
+        "cec_i_l_ref": 6.23681,
+        "cec_i_mp_ref": 5.85,
+        "cec_i_o_ref": 3.98831e-12,
+        "cec_i_sc_ref": 6.23,
+        "cec_is_bifacial": 0,
+        "cec_module_length": 1.631,
+        "cec_module_width": 1,
+        "cec_mounting_config": 0,
+        "cec_mounting_orientation": 0,
+        "cec_n_s": 96,
+        "cec_r_s": 0.499388,
+        "cec_r_sh_ref": 457.186,
+        "cec_standoff": 6,
+        "cec_t_noct": 46.4,
+        "cec_temp_corr_mode": 0,
+        "cec_transient_thermal_model_unit_mass": 11.0919,
+        "cec_v_mp_ref": 57.3,
+        "cec_v_oc_ref": 67.9
+      },
+      "CECPerformanceModelWithUserEnteredSpecifications": {
+        "sixpar_aisc": 0.004,
+        "sixpar_area": 1.956,
+        "sixpar_bifacial_ground_clearance_height": 1,
+        "sixpar_bifacial_transmission_factor": 0.013,
+        "sixpar_bifaciality": 0.65,
+        "sixpar_bvoc": -0.11,
+        "sixpar_celltech": 1,
+        "sixpar_gpmp": -0.41,
+        "sixpar_imp": 6,
+        "sixpar_is_bifacial": 0,
+        "sixpar_isc": 7,
+        "sixpar_mounting": 0,
+        "sixpar_nser": 60,
+        "sixpar_standoff": 6,
+        "sixpar_tnoct": 46,
+        "sixpar_transient_thermal_model_unit_mass": 11.0919,
+        "sixpar_vmp": 30,
+        "sixpar_voc": 37
+      },
+      "SandiaPVArrayPerformanceModelWithModuleDatabase": {
+        "snl_a": -3.5578,
+        "snl_a0": 0.9428,
+        "snl_a1": 0.0536,
+        "snl_a2": -0.01281,
+        "snl_a3": 0.001826,
+        "snl_a4": -0.0001048,
+        "snl_aimp": -0.0003,
+        "snl_aisc": 0.0004,
+        "snl_area": 1.65,
+        "snl_b": -0.1766,
+        "snl_b0": 1,
+        "snl_b1": -0.007861,
+        "snl_b2": 0.0009058,
+        "snl_b3": -3.496e-05,
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+          1e+38,
+          1e+38,
+          1e+38,
+          1e+38,
+          1e+38,
+          1e+38
+        ],
+        "grid_interconnection_limit_kwac": 100000
+      },
+      "AdjustmentFactors": {
+        "constant": 5,
+        "dc_constant": 0
+      },
+      "Outputs": {}
+    },
+    "latitude": 40.0,
+    "longitude": -100.0,
+    "elevation": 0,
+    "albedo": 0.4
+  }
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index ea8036a..5f6f840 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ mypy==0.950
 notebook==6.4.11
 nrel-pysam==3.0.2
 numba==0.55.2
-numcodecs==0.11.0
+numcodecs==0.13.0
 numpy==1.22.3
 openpyxl==3.0.7
 pandas==1.4.2
diff --git a/time_series_models/data_fetchers/ami_fetcher.py b/time_series_models/data_fetchers/ami_fetcher.py
index 4165854..a37a054 100644
--- a/time_series_models/data_fetchers/ami_fetcher.py
+++ b/time_series_models/data_fetchers/ami_fetcher.py
@@ -88,8 +88,8 @@ class AmiFetcher(Fetcher):
     """
 
     # TODO: update GCS_BUCKET and GCS_PATH_FORMAT depending on where prod data land
-    GCS_BUCKET = None
-    GCS_PATH_FORMAT = "Arroyo_Hondo_AMI_data/{units:s}/{meter:s}_{units:s}.csv.gz"
+    GCS_BUCKET = "seto2243-forecasting"
+    GCS_PATH_FORMAT = "ami_validation/{meter:s}_{units:s}.csv.gz"
 
     # for testing only # TODO(Michael H): extract to Fetcher class?
     FILE_SYSTEM_URIS = {}
diff --git a/time_series_models/data_fetchers/fetcher.py b/time_series_models/data_fetchers/fetcher.py
index 2668a38..2589928 100644
--- a/time_series_models/data_fetchers/fetcher.py
+++ b/time_series_models/data_fetchers/fetcher.py
@@ -259,7 +259,7 @@ def gcs_buffer_loader(cls, path):
     @classmethod
     def get_gcs_client(cls):
         if not cls._GCS_CLIENT:
-            client = storage.Client()
+            client = storage.Client.create_anonymous_client()
             adapter = requests.adapters.HTTPAdapter(
                 pool_connections=128, pool_maxsize=128, max_retries=3, pool_block=True
             )
diff --git a/time_series_models/data_fetchers/hrrr_fetcher.py b/time_series_models/data_fetchers/hrrr_fetcher.py
index 4d48f32..07ddc74 100644
--- a/time_series_models/data_fetchers/hrrr_fetcher.py
+++ b/time_series_models/data_fetchers/hrrr_fetcher.py
@@ -73,7 +73,11 @@ def load_hrrr_data(
     with dask.config.set(scheduler="threading"):
         ds = xr.open_dataset(
             rpath_mapper(blob_path),
-            drop_variables=["heightAboveGround"],  # Drop magic broken variable
+            drop_variables=[
+                "heightAboveGround",
+                "isobaricInhPa",
+                "surface",
+            ],  # Drop magic broken variable
             engine="zarr",
             backend_kwargs=dict(consolidated=False),
             chunks={"valid_time": 1},
@@ -170,11 +174,7 @@ def rpath_mapper(rpath: str | typing.Iterable[str]):
 # Source Mode names match suffix in GCS blobs
 # todo: for horizon mode, resolve underlying file formats and expose client to
 #  00horizon - 48horizon only?
-SOURCE_MODES = (
-    [f"{val:02}_hour_horizon" for val in range(0, 19)]
-    + [f"{val:02}-{val+5:02}_hour_horizon" for val in range(19, 44, 6)]
-    + ["18_hour_forecast", "48_hour_forecast"]
-)
+SOURCE_MODES = ["12_hour_horizon"]
 
 
 class HrrrFetcher(GriddedDataFetcher):
@@ -195,7 +195,7 @@ class HrrrFetcher(GriddedDataFetcher):
     have enough gridded data products to look for good patterns.
     """
 
-    HRRR_GCS_BUCKET = "gcp-public-data-weather"
+    HRRR_GCS_BUCKET = "seto2243-forecasting"
     # Must define storage in this class for location mappings
     LOCATION_MAPPING: dict = {}
 
@@ -207,7 +207,7 @@ def __init__(
         location_mapping: dict | None = None,
         resource_type: str | None = None,
         resource_query: str | None = None,
-        source_mode: str = "48_hour_forecast",
+        source_mode: str = "12_hour_horizon",
     ):
         """
         Initialize Fetcher base class and configure selector for variable selection or groupby operations.
@@ -286,50 +286,7 @@ def select_blob(self, start: np.datetime64, end: np.datetime64):
             hour,
         )
 
-        base = f"gcs://{self.HRRR_GCS_BUCKET}/high-resolution-rapid-refresh/version_2"
-        match self.source_mode:
-            case "18_hour_forecast":
-                if end - start > np.timedelta64(18, "h"):
-                    raise RuntimeError(
-                        f"Grid Point Fetcher configured with 18 hour model, "
-                        f"but requested range is {end - start}"
-                    )
-
-                return f"{base}/forecast_run/conus/hrrr.{year}{month:02}{day:02}/hrrr.t{hour:02}z.{self.model}.{self.source_mode}.zarr"
-
-            case "48_hour_forecast":
-                if end - start > np.timedelta64(48, "h"):
-                    raise RuntimeError(
-                        f"Grid Point Fetcher configured with 48 hour model, "
-                        f"but requested range is {end - start}"
-                    )
-
-                return f"{base}/forecast_run/conus/hrrr.{year}{month:02}{day:02}/hrrr.t{math.floor(hour/6) * 6:02}z.{self.model}.{self.source_mode}.zarr"
-
-            case _:
-                start_padded = start.astype("datetime64[M]") - np.timedelta64(1, "M")
-                end_padded = end.astype("datetime64[M]") + np.timedelta64(2, "M")
-                dates = np.arange(start_padded, end_padded, np.timedelta64(1, "M"))
-                dates = zip(
-                    TimeUnitEnum("Y").as_unit(dates),
-                    TimeUnitEnum("M").as_unit(dates),
-                )
-                blobs = []
-                fs = fsspec.filesystem("gcs", token=None)
-                for year, month in dates:
-                    blob = f"{base}/monthly_horizon/conus/hrrr.{year}{month:02}/hrrr.{self.model}.{self.source_mode}.zarr"
-                    if fs.exists(blob):
-                        blobs.append(blob)
-                    else:
-                        logger.debug("Data for month %s doesn't exist", blob)
-
-                if not blobs:
-                    raise FileNotFoundError(
-                        f"None of the requested monthly HRRR blobs could be found in {base}/monthly_horizon/conus/"
-                    )
-
-                logger.debug("Loading hrrr for: %s", blobs)
-                return blobs
+        return f"gcs://{self.HRRR_GCS_BUCKET}/high-resolution-rapid-refresh/hrrr_12_hour_horizon_2021.json"
 
     def source_loader(self, start, end, *domain_locations):
         """
diff --git a/time_series_models/processes.py b/time_series_models/processes.py
index b011ada..dd7c9ec 100644
--- a/time_series_models/processes.py
+++ b/time_series_models/processes.py
@@ -150,7 +150,7 @@ def __init__(self, lags: list | np.ndarray = None, **kwargs):
         # we don't want to interpolate missing labels -- these should be skipped during training and evaluation!
         self._energy_fetcher = AmiFetcher(
             location_mapper=lambda x: x,
-            units="watts",
+            units="energy",
         )
         self._net_energy_pipeline = net_energy_pipeline(self.energy_fetcher)
         self._overfetched_range_pipeline = overfetched_range_pipeline(
@@ -220,6 +220,8 @@ def make_preprocessor(
         day_of_week: bool = False,
         business_day: bool = False,
         harmonics: list | np.ndarray = None,
+        # mapping: typing.Callable | None = None,
+        mapping: dict | None = None,
         **kwargs,
     ):
         logger.info("creating lags %s", lags)
@@ -246,10 +248,12 @@ def make_preprocessor(
                     self._weather_fetcher_name,
                     HrrrFetcher(
                         location_mapper="RESOURCE_LOOKUP",
+                        location_mapping=mapping,
                         selector="select",
                         selector_args={"variables": met_vars},
                         source_mode=f"{met_horizon}_hour_horizon",
                         resource_type="meter/electrical",
+                        resource_query="No-Op",
                     ),
                     [0],
                 )
@@ -428,7 +432,7 @@ def __init__(
         # Optimization hack - don't bother trying to fetch the range when there is no mapping
         # Need to revisit once we have a mapping for some but not most PV systems
         # Set all the properties to point to the nan fetcher.
-        self._range_fetcher = None
+        self._range_fetcher = NanFetcher(None, None, None)
         self._overfetched_range_pipeline = (
             self._range_fetcher  # used in get_range and friends
         )
@@ -511,16 +515,16 @@ def make_preprocessor(self, lags=None, **kwargs):
                 make_pv_pipeline(self.hrrr_fetcher, self._pv_builder),
                 [0],
             ),
-            (
-                "correlated_generation",
-                autoregressive_features_pipeline(
-                    self.range_fetcher,
-                    lags,
-                    self.tstep,
-                    **kwargs,
-                ),
-                [0],
-            ),
+            # (
+            #     "correlated_generation",
+            #     autoregressive_features_pipeline(
+            #         self.range_fetcher,
+            #         lags,
+            #         self.tstep,
+            #         **kwargs,
+            #     ),
+            #     [0],
+            # ),
         ]
 
         # TODO add harmonics and other features
diff --git a/time_series_models/transformers_pv.py b/time_series_models/transformers_pv.py
index 85e1592..be9194b 100644
--- a/time_series_models/transformers_pv.py
+++ b/time_series_models/transformers_pv.py
@@ -94,6 +94,8 @@ def pv_function(X, pv_model_builder: PvModelBuilder):
         )
         dataframe.index.name = "date_time"
 
+        logger.info("Feature DF: %s", dataframe)
+
         pv_systems = pv_model_builder(location)
         if len(pv_systems) > 1:
             # Make a copy - pvsystem may do bad things to the dataframe